We propose clinical trials that will assess the effectiveness of using a combination of therapies to reduce symptom burden during aggressive cancer treatment. The target populations for study are patients undergoing chemoradiotherapy for head and neck cancer or lung cancer. The high symptom burden caused by aggressive treatment of these cancers may become so severe that patients may be forced to interrupt therapy, may need to make unscheduled visits for emergency symptom care, and/or may have difficulty maintaining vocational and family commitments during therapy and for weeks thereafter. The combined interventions we will test are derived from a theoretical model based on evidence that treatment-produced inflammation is a major cause for many symptoms, such as fatigue, appetite loss, emotional distress, and pain, and that modulation of this inflammation and its consequences will significantly reduce symptom burden. Combinations of therapy will include inhibitors of NF-B (a precursor molecule for inflammation), inhibitors of inflammatory cytokines, and an antidepressant (bupropion) that also has inflammatory action. We will include a wakefulness-promoting agent (modafinil) that may modify the effects of inflammatory cytokines on the brain. We will utilize new methods of assessment (a computer-based telephone assessment system combined with symptom-assessment questionnaires developed for each disease condition) to frequently monitor the severity of treatment-specific symptoms over the time of the study. Symptoms will be monitored twice a week using the telephone-based assessment system. This method will allow us to derive an area under the curve (AUC) for multiple symptoms for each patient group across the period of treatment. Changes in AUC for established severe treatment-related symptoms for each group will be the primary trial outcome variable. A key component of this proposal is the use of a Bayesian adaptive design to rapidly assess the efficacy of the several treatments and treatment synergies. Such a design take advantage of accumulating trial results by assessing them periodically; the trial can then be adjusted by slowing (or stopping) ineffective interventions or by expanding patient accrual to better-performing therapies, potentially leading to smaller, more informative trials and better treatments for patients. With now-available increases in computational power and newer modeling methods, these adaptive methods are increasingly being used in clinical research for curative therapies but have yet to be used in symptom research, where their advantages in maximizing treatment benefits within the trial period are attractive. The Bayesian adaptive approach to the assessment of symptom-focused treatments may be an inportant avenue to address the critical need for an evidence base for clinical decisions about symptom control. The significance of this research is that it addresses methods of reducing the symptoms and side effects of aggressive cancer therapy. We will use new developments in clinical trial design that let us identify best combined treatments for symptom reduction as quickly as possible, and let us administer the effective treatment combinations to more patients within the period of the clinical trial. We aim to develop a strategy using several drugs working together to prevent or ameliorate multiple treatment-related symptoms, such as fatigue, pain, sleep disturbance, and poor appetite, for patients treated for head and neck cancer or lung cancer. The ultimate goal of this research is to be able to administer the best available curative therapy to the largest number of patients with the least treatment-related symptom burden.